Recently, there is a growing concern about influenza A, especially subtypes H1N1 and
H5N1 for their high transmission and mortality rates. Shikimic acid (3,4,5-trihydroxy-1-cyclohexene-1-carboxylic
acid), [Fig. 1], is a natural organic compound and an important intermediate in the biochemical
pathways of plants and microorganisms. It is generally used as a starting material
for industrial synthesis of the antiviral Oseltamivir (Tamiflu®, used against influenza
A) and as a reactant in organic synthesis [1], [2]. According to Chen et al. [3], shikimic acid and its derivatives possess several pharmacological effects, such
as antithrombotic, anti-inflammatory, analgesic, antioxidant, anticancer, and antibacterial
effects. However, shikimic acid is a scarce and expensive chemical, obtained mainly
from the seeds of Illicium verum Hook.f. (Schisandraceae), a shrub native in China, and Illicium anisatum Gaertn., native to Japan [4]. So, there is an urgent need for the production of large amounts of shikimic acid
from different sources [5].
Fig. 1 Shikimic acid.
Glyphosate is an herbicide widely used for controlling weeds around the world, whose
mechanism of action is attributed to the activity inhibition of 5-enolpyruvyl-shikimic
acid-3-phosphate (EPSP) synthase, an enzyme of the shikimic acid pathway, in a wide
variety of plants with consequent shikimic acid accumulation. Glyphosate is the only
commercially available herbicide that inhibits the enzyme EPSP, blocking the synthesis
of aromatic amino acids [6]. The majority of the plant species are sensitive to glyphosate and when treated
with this herbicide, accumulate high levels of shikimate [7], [8].
Although glyphosate is currently the most important active ingredient for controlling
weeds, low-dose applications offer the commercial use of glyphosate as a growth regulator
[9]. A hormesis effect of glyphosate has been observed in various plants [10], [11], [12], [13], [14].
Studies on the increase of shikimic acid production in plants by treatment with low
doses of glyphosate can be cited [15], [16], [17], [18]. Recently, Franco et al. [19] investigated the possibility of using Brachiaria plantaginea (Link) Hitchc. (Poacaea) as another alternative source of shikimic acid. B. plantaginea is an annual grassy plant, common in Brazil and other countries in South America,
Mexico, northeast and southeast USA, and Africa. In Brazil it is cultivated for forage,
producing a rapid spring growth and allowing up to three cuts per cycle. The phenotypic
characteristics and soil and climate requirements allow cultivation virtually throughout
the national territory [20], [21].
In this work, 44 samples of B. plantaginea were analyzed, and shikimic acid was quantified by high-performance liquid chromatography
(HPLC) ([Table 1]). Although HPLC is versatile, quantitative, and precise, it can also be time-consuming
and costly, requiring large quantities of expensive and toxic organic solvents [22], [23].
Table 1 Results of HPLC analysis in B. plantaginea samples.
|
a g acid equivalent (a. e.) of glyphosate ha− 1; b standard deviation of triplicates
|
|
Dose glyphosate (g × ha−1)a
|
Days after treatments with glyphosate
|
|
3
|
6
|
9
|
12
|
|
Mean values of shikimic acid concentration (µg × g−1)
|
|
0
|
604.21 (128.81)b
|
717.15 (185.92)b
|
892.10 (110.83)b
|
629.37 (309.76)b
|
|
0.36
|
827.74 (218.15)b
|
541.86 (74.59)b
|
867.10 (38.74)b
|
693.06 (49.87)b
|
|
3.6
|
710.74 (87.55)b
|
892.09 (163.07)b
|
998.73 (83.44)b
|
402.42 (50.04)b
|
|
36
|
1724.37 (481.76)b
|
3187.91 (368.87)b
|
1780.48 (164.71)b
|
2013.22 (459.47)b
|
Alternative methods based on near infrared spectroscopy (NIR) have been widely used
in the determination and quantification of constituents of agricultural samples [22], [24], [25], [26], [27]. NIR spectroscopy is fast and nondestructive; however, the overtone and combination
bands seen in the near-IR spectra are typically very broad, leading to complex spectra.
Therefore, multivariate calibration techniques such as partial least square regression
(PLS) are often employed to extract the desired chemical information. Among the techniques
for employing the NIR spectral region, diffuse reflectance is one of the most used
[27], [28].
The main objectives of this study were investigate the glyphosate dose and exposure
period of the B. plantaginea to the herbicide that would result in the greatest accumulation of shikimic acid,
then, propose a fast and clean procedure for the quantification of shikimic acid in
samples of B. plantaginea using NIR combined with PLS.
The experimental results presented in [Table 1] show a higher accumulation of shikimic acid after 6 days of glyphosate application
at the concentration of 36.0 g acid equivalent of glyphosate ha−1. The rates of glyphosate and the maximum period of shikimic acid accumulation are
consistent with the literature [29], [30], [31], [32]. The high standard deviation observed in [Table 1] probably resulted from the intrinsic level of shikimic acid in plants associated
with external factors. Chen et al. [33], studying the shikimic acid contents in conifer needles from different regions in
China, observed great differences within the same or different species, even in the
same region, the shikimic acid content of samples had distinct variances.
Shikimic acid is produced as an intermediate metabolite in the biochemical pathways
of plants, and for the same dose of herbicide and same harvest time, variations in
the amount of shikimic acid produced by the individuals studied were observed, suggesting
differential sensitivity to glyphosate due to a higher demand for shikimate pathway
products. A greater sensitivity to glyphosate, and consequently a greater shikimate
accumulation, was detected by Pline et al. [34] in reproductive tissue over vegetative tissue. Furthermore, drifts of glyphosate
applications are common and consequently the results are also subject to variations,
which may lead to acid concentration values different from those expected. Shikimate
accumulation in corn decreased from 349 % at 0 m to 93 % at 15.8 m, and shikimate
levels were unaffected beyond 25.6 m downwind from a single aerial application of
866 g a.e × ha−1 of glyphosate (Reddy et al.) [35].
The greatest accumulation of shikimic acid was observed in B. plantaginea sprayed with glyphosate at 36.0 g a.e × ha−1. The peak concentration occurred within 6 days of exposure to the herbicide and was
345 % higher compared to unsprayed plants. These results confirm the potential of
this plant as a source of shikimic acid.
Before building the regression model, principal component analysis was performed in
the mean-centered spectral data (X matrix) to check for trends or clusters among samples and the presence of outliers.
The first two PCs explain about seventy percent of the total variance. Analyzing the
two-dimensional PC1 vs. PC2 scores plot based on the mean-centered data ([Fig. 2]), one can observe that sample 46 is outside the 95 % confidence ellipse, but its
spectrum does not show atypical features. A significant overlap with a tendency towards
discrimination of plants harvested at 6, 9, and 12 days after treatment with glyphosate
along PC1 can be seen in this plot. The scores tend to become more positive in the
first principal component with increasing time of exposure to glyphosate. When the
samples with an exposition time of 3 days are included in the PC analysis, they occupy
an intermediate region in the PC1 vs. PC2 scores plot, but tend to be discriminated
mainly by PC2 (Fig. 1 S, Supporting Information).
Fig. 2 PC1 vs. PC2 scores plot of mean-centered NIR spectra of samples collected at 6 (○),
9 (*) and 12 (▴) days after treatment with glyphosate.
The dendrogram ([Fig. 3]) shows three major groups with a similar value around 0.6. These clusters, G1, G2
and G3, are mainly composed of samples harvested at 12, 6, and 3 days, respectively,
after treatment with glyphosate, but independent of the applied dose. In conclusion,
exploratory data analysis showed some tendency to discriminate samples with respect
to the harvest time, but not with the glyphosate dose applied.
Fig. 3 Dendrogram of the spectral data of 44 B. plantaginea samples. G1, G2, and G3 are the groups consisting mainly of samples harvested after
12, 6, and 3 days, respectively, of treatment with glyphosate herbicide. G1 and G3
showed a good classification of samples, while G2 is smaller and was formed by a mixture
of samples harvested after 6 and 9 days. Samples 3, 21, and 33 in G1 were incorrectly
classified, having been harvested with 3, 9, and 9 days, respectively. G3 contains
eleven samples, whereas only two (32 and 41) were incorrectly classified. The remaining
samples formed smaller groups with only two or three components, or are mixed in larger
clusters.
The PLS model was built using the shikimic acid concentration values determined by
HPLC and the spectral data. The final model was obtained after removing four outliers
(samples 29, 33, 34, and 44) from the data set. After the removal of the outliers,
the PLS model was built with 29 samples in the training set and the remaining 11 comprising
the test set. The parameters for model evaluation are displayed in [Table 2].
Table 2 Parameters for model evaluation and validationa of the best PLS model obtained.
|
Matrix size
|
Pretreatment
|
Factors
|
Outliers
|
R2
|
SEC
|
SECV
|
SEP
|
RSD %
|
RER
|
|
Cal
|
Val
|
Pred
|
|
a Original 44 samples, 4 outliers, 29 in training, and 11 in validation sets
|
|
44
|
Smoothing, first deriv.
|
7
|
4
|
0.99
|
0.84
|
0.93
|
84.05
|
349.53
|
154.91
|
13.03
|
9.42
|
Among all pretreatments applied to each mean spectrum, the smoothing and first derivative
with windows of 7 and 13 points, respectively, based on a Savitzky-Golay polynomial
filter [36], resulted in the best model. Leave-one-out cross-validation was applied during the
PLS modeling in order to determine the number of factors in the model based on predictive
ability. The optimum number of factors is indicated by the local minimum in the plot
of standard error of cross-validation (SECV) vs. the number of factors (Fig. 2 S, Supporting Information). [Table 2] shows the coefficients of determination for calibration and validation (R2
Cal and R2
Val), SECV, and the standard error of calibration (SEC) for the final model with seven factors.
The regression vector (Fig. 3 S, Supporting Information) shows the positive and negative coefficients in the model.
We can observe the signals corresponding to the combination bands of lignin (C–H stretching)
and the peaks in the region of the combination bands of O-H and C=O stretches of carboxylic
acids [37], [38].
[Fig. 4] shows the plots of reference vs. predicted values for calibration and external validation
sets. The linearity observed indicates that the model with seven factors shows a good
fitting, although the validation set presents a tendency to overestimate the shikimic
acid concentration values, with a bias of − 10.55 (Table 1 S, Supporting Information).
Fig. 4 Plot of reference vs. predicted values for (■) calibration and (*) external validation of shikimic acid in B. plantaginea sample models with seven factors.
The reasonable agreement between reference vs. predicted values for calibration and
external validation sets indicates that the final model can be used for an approximate
prediction of new samples. The range error ratio (RER) value was 9.42, an indication for quality control, according to the American Association
of Cereal Chemists (AACC) – Method 39–00 [39]. The relative standard deviation (RSD%) was 13 % and the percent of calibration error range (RE%) varied from 1 to 10 % for most of the samples, although two samples presented an
error greater than 20 %. For the external validation set, the mean prediction error
was 10 %. The difficulty in getting a better model can be explained in part by the
wide range of shikimic acid concentrations (333.90 to 3592.45 µg × g−1).
Despite the difficulty in establishing a PLS model for this data set, the results
obtained for both calibration and prediction sets were satisfactory, demonstrating
that NIR spectroscopy associated with PLS regression is a possible alternative to
quantify shikimic acid in B. plantaginea. The model was considered validated, as shown by the results found for the external
set of samples. The coefficient of determination was above 0.90 and the RER value was close to 10.0, indicating that the proposed model is acceptable for quality
control.
Material and Methods
Samples: The trial was conducted in a greenhouse. Seeds of B. plantaginea were planted in vessels on loamy soil classified as purple Eutrustox under a completely
randomized design with three replications. Prior to planting, fertilization was performed
with 260 kg × ha−1 of 00–20–20 (N–P-K) fertilizer, and the herbicide glyphosate (Roundup Original) was
sprayed at reduced doses of 36.0, 3.6, and 0.36 g acid equivalent (a. e.) of glyphosate
ha−1, which are very low compared to the minimum dose suggested as herbicide by Rodrigues
and Almeida (360 g a. e. × ha−1) [40].
Treatments were applied at the beginning of the reproductive stage of B. plantaginea, 71 days after sowing, using a CO2 backpack sprayer equipped with four nozzles spaced at 0.5 m and 110.03 tips (Magno)
at a constant pressure of 35 lb × in−2 adjusted for a spray volume of 100 L × ha−1. At the time of spraying, wind force, according to the Beaufort scale, varied between
4.0 and 5.0 km × h−1; air temperature was 24 °C and relative air humidity was 65 %.
The determination of shikimic acid in B. plantaginea leaves followed the procedure described by Matallo et. al. [41]. At 3, 6, 9, and 12 days after treatments, the leaves were collected, always in
the morning, dried in a forced air circulation oven at 60 °C for 48 h, and then crushed
at 25 000 rpm (IKA A11 Basic), frozen at temperatures ≤ 10 °C, stored in polystyrene
boxes, sealed, and transported to the laboratory. The extraction of shikimic acid
was performed in replicates of 400 mg of dry matter, mixed with 10 mL of water, acidified
to pH 2 with phosphoric acid (Merck), heated in a microwave oven (Panasonic NN-S62B)
at 100 W for 10 seconds at a temperature of 49.8 °C ± 2.8 °C, allowed to cool for
5 min and then was filtered through Whatman N. 1 filter paper and a 0.22-µm membrane
filter (Millex-GV, Millipore) for quantification. Shikimic acid was quantified by
HPLC ([Fig. 5 A]) using a Shimadzu LC 2010 A chromatographer (isocratic mode) with a diode array
detector at a wavelength of 212 nm, injection volume of 20 µL, and a Phenomenex Gemini
C18 110 Å (250.0 mm × 4.0 mm; 5 µm particle size) LC column. The mobile phase used
was ultrapure water acidified solution with phosphoric acid and methanol (95 : 5)
at a flow rate of 1.0 mL × min−1. It was verified that the chromatograms were reproducible and so each replicate was
analyzed only once by HPLC.
Fig. 5 A Chromatogram of shikimic acid (black) in B. plantaginea and its standard (cyan). B Generic NIR spectrum of B. plantaginea.
The results were taken on a dry weight basis in order to eliminate the variability
of the water content of the plants. Data were subjected to analysis of variance by
the F-test ([Table 1]).
Near infrared spectroscopy data analysis: The diffuse reflectance spectra in the near infrared region were acquired by using
an Antaris II FT-NIR spectrometer (Thermo Fisher Scientific) in the 4500 to 10 000 cm−1 range. The spectra were generated by averaging 16 successive scans with 4 cm−1 nominal resolution (yielding 3112 wavenumbers). The signals were obtained in the
reflectance mode (%R) and transformed into absorbance by using a log transformation.
[Fig. 5 B] shows a generic spectrum. Three spectra were recorded for each sample, and the average
spectrum was used for data analysis.
The mean spectra were organized in a matrix format X (44, 3112) where each row corresponds to a sample and the columns correspond to the
absorbance (log 1/R) values. Different mathematical transformations were tested; the
best results were obtained by applying smoothing and the first derivative. The vector
y of the concentrations was correlated with spectral information through the PLS regression
method on the mean-centered data using Pirouette® 4.5 software. Leave-one-out cross-validation
was used to determine the number of factors (latent variables) in the calibration
model. The presence of outliers was investigated by analyzing the plot of leverage
vs. studentized residuals and by principal component analysis. After removing the
outliers, the data set was randomly split into two subsets: the training (calibration)
set containing 29 samples and the test set with the remaining 11. The final regression
model was evaluated by analyzing the values of R2
, SEC, SECV, and the relative error (RE%) as shown in Table 2 S, Supporting Information. An external validation set was used to evaluate the prediction
ability of the model through the standard error of prediction (SEP), the RSD (RSD% = SEP × 100/mean, where the mean is taken from reference values in the validation set),
the RER (RER = Rangey of reference data/SEP) [39], and the R2
. The equations for the calculation of the validation parameters are shown in Table 2 S, Supporting Information.
Supporting information
The relative error values for the complete set of 40 samples, the equations for the
calculation of the validation parameters, and PC1 vs. PC2 scores plot of the mean-centered
NIR spectra of the samples are available as Supporting Information.
